| Hedonic Regression |
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Information AboutHedonic Regression |
| CATEGORIES ABOUT HEDONIC REGRESSION | |
| real estate | |
| urban economics | |
| econometrics | |
| economic data | |
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It is commonly used in Real Estate Economics and Consumer Price Index (CPI) calculations. In consumer price index calculations hedonic regression is used to control the effect of changes in product quality. Price changes that are due to substitution effects are subject to hedonic quality adjustments. Some economists, primarily of the Austrian School , have critcized the US government's use of hedonic regression in computing its CPI, fearing it can be used to mask the "true" inflation rate and thus lower the interest it must pay on Treasury Inflation-Protected Securities (TIPS) and Social Security cost of living adjustments. In real estate economics, it is used to adjust for the problems associated with researching a good that is as heterogeneous as buildings. Because buildings are so different, it is difficult to estimate the demand for buildings generically. Instead, it is assumed that a house can be decomposed into characteristics such as number of bedrooms, size of plot, or distance to the city center. A hedonic regression equation treats these attributes (or bundles of attributes) separately, and estimates prices (in the case of an additive model) or elasticity (in the case of a log model) for each of them. This information can be used to construct a price index that can be used to compare the price of housing in different cities, or to do time series analysis. As with Consumer Price Index (CPI) calculations, hedonic pricing can be used to correct for quality changes in constructing a housing price index. It can also be used to assess the value of a property, in the absence of specific market transaction data. It can also be used to analyse the demand for various housing characteristics, and housing demand in general. It has also been used to test assumptions in spatial economics. SEE ALSO EXTERNAL LINKS
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